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HL MARKETING THEORY SALES FORCASTING

HL MARKETING THEORY SALES FORCASTING. IB BUSINESS & MANAGEMENT: A COURSE COMPANION, 2009: P196-199. SALES FORCASTING. As well as knowing where sales are now, it is very useful for businesses to know what they are likely to be in the future.

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HL MARKETING THEORY SALES FORCASTING

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  1. HL MARKETING THEORYSALES FORCASTING IB BUSINESS & MANAGEMENT: A COURSE COMPANION, 2009: P196-199

  2. SALES FORCASTING • As well as knowing where sales are now, it is very useful for businesses to know what they are likely to be in the future. • This helps businesses to decide the most appropriate marketing strategy to take advantage of the current situation and prepare themselves for future sales trends.

  3. SALES FORCASTING Extrapolation • Many businesses use extrapolation to help them get a better idea of what future performance is likely to be finding trends from past data. • The easiest way of extrapolating data is to plot previous sales figures on a graph and simply extend the trend line by hand. • Sales figures are easily accessible for businesses and a line of best fit can be drawn very quickly and easily. • This simplistic method can be quite effective for the near future, but it is very unlikely that an industry will have such stable growth patterns over the longer term.

  4. SALES FORECASTINGIdentifying Trends • Identifying trends is not always as simple as merely extending a line of best fit though. • Eg: Many industries have seasonable sales, such as the ice cream industry. • It would be pointless to compare summer sales with winter sales, as you would clearly expect the hot weather to mean that more ice-cream is being purchased. • Because of the issue of variations, many businesses use moving averagesto try to isolate trends due to factors such as seasons and economic cycles. This helps a firm to understand how the business has performed, and it is likely to perform in the future.

  5. SALES FORCASTINGTable Analysis from slide 5 • Sales figures for an imaginary swimwear company in France have been recorded in the supplied graph. • In France, the sales of swimwear peak in the summer – this is shown as quarter 3 in the data (July, August, September) • As a result, the trend is distorted by these seasonal sales. • In order to get a meaningful trend in sales, it is necessary to create an average. • Although it’s possible simply to take four quarters and calculate the average, this would pose the problem of which quarter to associate the average with, as there is no mid point of four quarters

  6. SALES FORCASTINGTable Analysis from Slide 5 Statistical Method: Centring • Each set of four quarters is added together to create a four-quarter total.

  7. SALES FORCASTINGTable Analysis from Slide 5 • In the example of the swimwear company, the 176,000 has come from the sum of the first four quarters in the data set. • The second total (173,000) comes from taking 2006 quarter to 2007 quarter 1, and so on. • Adding up adjacent four-quarter totals gives an eight quarter total, but note that the data has come from just five different quarters.

  8. SALES FORCASTINGTable Analysis – Slide 5 • The 349,000 is the sum of the 176,000 and 173,000 four quarter totals – comprised of two lots of 2006 quarter 1 and 2007 quarter 1. • This means that the data now has a mid-point and the average of the eight quarter total (43,625) can be assigned to it.

  9. SALES FORCASTINGTable Analysis – Slide 5 • By continuing this process, you can see the sales trend after the seasonal variation has been stripped out. • Plotting this on a graph allows for the line of best fit to be extrapolated into the future. • The moving average line shows far more clearly what is generally happening to sales than can be seen by the wildly fluctuating sales figures.

  10. SALES FORCASTINGTable Analysis – Slide 5 • You must remember, though, that if this trend is to be extrapolated into the future, it will not show actual sales figures, as the seasonal variations have been removed. • To get predicted sales figures for the future, you must extrapolate the trend and add on or take away the average seasonal variation.

  11. SALES FORCASTINGTable Analysis – Slide 5 • This is calculated by averaging the difference between the actual sales and the trend in any given quarter. • As a result, summer (quarter 3) sales could be extrapolated by extending the moving average line and adding 29,958 (the average seasonal variation for summer) • For quarter 4 sales, you would need to extrapolate the trend and take away 4,333 as winter sales are lower than the moving average.

  12. Using date from the table we can construct a Sales and Moving Average Graph. This provides an excellent visual reference of on our real sales situation.

  13. PRACTICAL EXERCISE: Using the information below, create Sales Data Table:

  14. Draw a Sales & Moving Average Graph(Based on the previous calculations in the previous slide)

  15. SALES FORCASTINGIs extrapolation subjective?? • To some extent, the extrapolation will also be subjective. • If you have had booming sales growth, you may hope that this continues into the future, but it may well not: • Therefore many businesses given an optimistic (best-case) and pessimistic (worst-case) prediction of future sales to take account of the uncertainty involved in predicting the future.

  16. SALES FORCASTINGRandom Variation • Any variation not explained by the average seasonal variation is known as the random variation and is down to factors other than the seasonal boost in sales in swimwear. • In quarter 3 2008 for example, sales were higher not only because of the summer weather, which accounts for 29,958 of the increase, but were in fact another 1,667 higher, due to other factors.

  17. SALES FORCASTINGCyclical Variations • Businesses can look at cyclical variations which take account of the ups and downs in the business cycle, by extrapolating an average trend, and then adding an extra allowance for the extra sales in a boom, or taken away the reduction in sales expected in a recession.

  18. SALES FORCASTINGResponding to Data • However, the data is calculated, it is fundamental that a business acts on it. • A falling trend will need to be remedied by adjusting the marketing mix, for example, increasing potential spending. • Higher trend growth may indicate that the company could consider increasing prices to gain additional profit from a product in demand. • The data in themselves will help in the decision making process, but qualitative factors, such as customer perceptions and brand loyalty, will also need to be taken into account before the strategy is implemented.

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